A review and meta-analysis of anti-ribosomal P autoantibodies in systemic lupus erythematosus
Bibliographic record
Abstract
The discovery of autoantibodies to ribosomal proteins (anti-RibP) dates back more than fifty years when antibodies to ribosomes were identified in systemic lupus erythematosus (SLE) sera. Over the years, anti-RibP autoantibodies have been the subject of extensive study and became known as a highly specific biomarker for the diagnosis of SLE and were associated with neuropsychiatric SLE (NPSLE), lupus nephritis (LN) and hepatitis (LH). As demonstrated by studies on cultured human cells and of murine models, there is evidence to suggest that anti-RibP may have a pathogenic role in LN and NPSLE. Despite a wealth of evidence, in comparison to other SLE autoantibodies such as anti-Sm and anti-dsDNA, anti-RibP has not been included in classification criteria for SLE. A significant challenge is the variability of assays used to detect anti-RibP, including the antigens and diagnostic platforms employed. This may account for the marked variation in frequencies (10-47%) in SLE and its association with clinical and demographic features reported in SLE cohorts. We performed a systematic literature review and meta-analysis to help clarify its prevalence, various clinical and serological associations in SLE based on the different RibP antigens and assay platforms used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".